Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Eng    2024, Vol. 11 Issue (2) : 288-310    https://doi.org/10.1007/s42524-024-3061-7
Construction Engineering and Intelligent Construction
Adaptive pandemic management strategies for construction sites: An agent-based modeling approach
Chengqian LI1, Qi FANG2, Ke CHEN3, Zhikang BAO4, Zehao JIANG3(), Wenli LIU3
1. School of Civil Engineering, Hunan University, Changsha 410082, China
2. School of Civil Engineering, Central South University, Changsha 410004, China
3. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
4. School of Energy, Geoscience, Infrastructure, and Society, Heriot-Watt University, Edinburgh, UK
 Download: PDF(14015 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

In the face of sudden pandemics, it becomes crucial for project managers to quickly adapt and make informed decisions that anticipate the consequences of their actions. This highlights the need for proactive management strategies to enhance epidemic response efforts. However, current research mainly emphasizes the negative impacts of pandemics, often neglecting the development of adaptable management approaches for construction sites. This study aims to fill this research void by developing strategies tailored to managing pandemics at construction sites. Using agent-based modeling, the study simulates the movement patterns of workers and the consequent spread of an epidemic under different risk scenarios and management tactics. The results indicate that measures such as wearing masks, managing group activities, and enforcing entry controls can significantly reduce epidemic spread on construction sites, with entry controls showing the greatest effectiveness.
Keywords epidemic transmission      agent-based modeling      safety management      management strategy     
Corresponding Author(s): Zehao JIANG   
Just Accepted Date: 24 April 2024   Online First Date: 29 May 2024    Issue Date: 26 June 2024
 Cite this article:   
Chengqian LI,Qi FANG,Ke CHEN, et al. Adaptive pandemic management strategies for construction sites: An agent-based modeling approach[J]. Front. Eng, 2024, 11(2): 288-310.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-024-3061-7
https://academic.hep.com.cn/fem/EN/Y2024/V11/I2/288
Fig.1  The technical flowchart of this study.
Fig.2  Hierarchical clustering procedure for spatiotemporal trajectories.
Fig.3  The driving forces behind the movement of workers on construction sites.
Fig.4  Driving forces behind the movement of workers on construction sites.
Parameter reference Parameters Value
Mass of a worker mi 59–89 kg
Desired speed vi0 1.2–1.8 m/s
Relaxation time τi 0.4–0.6 s
Diameter of a person ri 0.375 m
Social force parameter A 2000 N
Social force parameter B 0.3 m
Social force parameter k 2.4 × 104 kg/s2
Social force parameter κ 1
Tab.1  Social force model calibration parameters (Li et al., 2015; Sticco et al., 2021)
Fig.5  SEIAR model for infectious diseases.
Fig.6  Construction site layout.
System elements Classification and quantity General attributes Features
Worker agents Carpenters (32)
Reinforcement workers (18)
Cement workers (13)
Scaffolders (9)
Machine operators (8)
Construction engineers (6)
Office staffs (2)
Quality control inspector (1)
Project manager (1)		 Face mask wearing indicator
Health status (susceptible, exposed latency, symptomatic infectious, asymptomatic infectious, recovered)		 Dynamic positions
Project size specification The Project site 110 m × 40 m Fixed positions
Working places Construction area (4)
Formwork stacking area (1)
Rebar processing shed (1)
Rebar stacking area (1)
Store house (1)
Waste area (1)
Office building (1)
Canteen (1)
Dormitory (1)
Quarantine area (1)		 Centroid position
Length
Width
Height		 Fixed positions
Tab.2  The system elements in the proposed model
Fig.7  Location monitoring platform for construction workers.
Fig.8  Spatiotemporal pattern mining of construction workers.
Fig.9  Transition probabilities among different working areas.
Target area Job type influence Start area influence
Construction area Significant (p = 0.00e + 00) Not significant (p = 5.37e−01)
Formwork stacking area Significant (p = 0.00e + 00) Not significant (p = 1.25e−01)
Rebar processing shed Significant (p = 1.66e−194) Not significant (p = 7.44e−01)
Rebar stacking area Significant (p = 2.26e−199) Significant (p = 4.58e−02)
Store house Significant (p = 2.39e−43) Not significant (p = 5.15e−01)
Waste area Significant (p = 8.03e−161) Not significant (p = 2.20e−01)
Office building Significant (p = 0.00e + 00) Not significant (p = 5.67e−01)
Tab.3  ANOVA summary and interpretations
Fig.10  Average stay time for different job types and working areas.
Fig.11  Workflow diagram of carpenters under the balancing group strategy.
Job types Working places and their probabilities
Carpenters Construction area (60%), Formwork stacking area (30%), Store house (5%), Waste area (5%)
Reinforcement workers Construction area (60%), Rebar processing shed (15%), Rebar stacking area (15%), Store house (5%), Waste area (5%)
Cement workers Construction area (90%), Store house (5%), Waste area (5%)
Scaffolders Construction area (90%), Store house (5%), Waste area (5%)
Machine operators Construction area (90%), Waste area (10%)
Construction engineers Construction area (60%), Office building (30%), Formwork stacking area (2.5%), Rebar processing shed (2.5%), Rebar stacking area (2.5%), Store house (2.5%)
Office staffs Construction area (10%), Office building (90%)
Quality control inspectors Construction area (70%), Office building (20%), Formwork stacking area (2.5%), Rebar processing shed (2.5%), Rebar stacking area (2.5%), Store house (2.5%)
Project manager Construction area (30%), Office building (60%), Formwork stacking area (2.5%), Rebar processing shed (2.5%), Rebar stacking area (2.5%), Store house (2.5%)
Tab.4  The working place distribution of various job types and their probabilities
Fig.12  Two main methods of epidemic transmission at construction sites.
Random Walking Purposed-based Movement
Movement mode of each agent Be instructed to move to a random location within the construction site every 5 min Moving based on the agent’s job types and the surrounding environments, as described in Fig.11 and Tab.4
Initial infected number of agents 1
Simulation runs 2000
Face mask wearing ratios 0.6
Group balance strategy Nongroup balancing
Other settings Listed in Tab.2
Tab.5  The experimental settings of different movement modes
Fig.13  Number of infections under different movement modes.
Fig.14  Protective efficacy of face masks against the epidemic.
Fig.15  Protective efficacy of the group balance strategy against the epidemic.
Fig.16  Performance of various management strategies.
Fig.17  Infection heatmap of workers at the construction site.
1 M O Alfadil, M A Kassem, K N Ali, W Alaghbari, (2022). Construction industry from perspective of force majeure and environmental risk compared to the COVID-19 outbreak: A systematic literature review. Sustainability, 14( 3): 1135, 1–22
https://doi.org/10.3390/su14031135
2 L T Allan-Blitz, I Turner, F Hertlein, J D Klausner, (2020). High frequency and prevalence of community-based asymptomatic SARS-CoV-2 infection. MedRxiv, 20246249
https://doi.org/10.1101/2020.12.09.20246249
3 A J Allen, M C Boudreau, N J Roberts, A Allard, L Hébert-Dufresne, (2022). Predicting the diversity of early epidemic spread on networks. Physical Review Research, 4( 1): 013123
https://doi.org/10.1103/PhysRevResearch.4.013123
4 A Alsharef, S Banerjee, S M J Uddin, A Albert, E Jaselskis, (2021). Early impacts of the COVID-19 pandemic on the United States construction industry. International Journal of Environmental Research and Public Health, 18( 4): 1559
https://doi.org/10.3390/ijerph18041559
5 B M Althouse, E A Wenger, J C Miller, S V Scarpino, A Allard, L Hébert-Dufresne, H Hu, (2020). Superspreading events in the transmission dynamics of SARS-CoV-2: Opportunities for interventions and control. PLoS Biology, 18( 11): e3000897
https://doi.org/10.1371/journal.pbio.3000897
6 L An, V Grimm, A Sullivan, II B L Turner , N Malleson, A Heppenstall, C Vincenot, D Robinson, X Ye, J Liu, E Lindkvist, W Tang, (2021). Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457: 109685
https://doi.org/10.1016/j.ecolmodel.2021.109685
7 M AntunesJ RibeiroD GomesR L Aguiar (2018). Knee/Elbow Point Estimation through Thresholding. IEEE, 413–419
8 F Araya, (2021a). Modeling the spread of COVID-19 on construction workers: An agent-based approach. Safety Science, 133: 105022
https://doi.org/10.1016/j.ssci.2020.105022
9 F Araya, (2021b). Modeling working shifts in construction projects using an agent-based approach to minimize the spread of COVID-19. Journal of Building Engineering, 41: 102413
https://doi.org/10.1016/j.jobe.2021.102413
10 F Araya, (2022). Modeling the influence of multiskilled construction workers in the context of the COVID-19 pandemic using an agent-based ap-proach. Revista de la construcción, 21( 1): 105–117
https://doi.org/10.7764/RDLC.21.1.105
11 S Aslan, O H Türkakın, (2022). A construction project scheduling methodology considering COVID-19 pandemic measures. Journal of Safety Research, 80: 54–66
https://doi.org/10.1016/j.jsr.2021.11.007
12 A Atkeson, (2020). On using SIR models to model disease scenarios for COVID-19. Federal Reserve Bank of Minneapolis Quarterly Review, 41( 01): 1–35
https://doi.org/10.21034/qr.4111
13 C Bohk-EwaldC DudelM. Myrskylä A demographic scaling model for estimating the total number of COVID-19 infections. medRxiv, p. 2020.04.23.20077719, 2020, doi: 10.1101/2020.04.23.20077719
14 B Briggs, C J Friedland, I Nahmens, C Berryman, Y Zhu, (2022). Industrial construction safety policies and practices with cost impacts in a COVID-19 pandemic environment: A Louisiana DOW case study. Journal of Loss Prevention in the Process Industries, 76: 104723
https://doi.org/10.1016/j.jlp.2021.104723
15 L CasiniG Manzo (2016). Agent-based models and causality: A methodological appraisal. Linköping University Electronic Press.
16 for Disease Control CentersPrevention (2022). How to determine a close contact for COVID-19.
17 D Centola, (2020). Considering network interventions. Proceedings of the National Academy of Sciences of the United States of America, 117( 52): 32833–32835
https://doi.org/10.1073/pnas.2022584118
18 I Cooper, A Mondal, C G Antonopoulos, (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons, and Fractals, 139: 110057
https://doi.org/10.1016/j.chaos.2020.110057
19 E Cuevas, (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in Biology and Medicine, 121: 103827
https://doi.org/10.1016/j.compbiomed.2020.103827
20 J P Devarajan, A Manimuthu, V R Sreedharan, (2023). Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics. IEEE Transactions on Engineering Management, 70( 9): 3229–3243
https://doi.org/10.1109/TEM.2021.3076603
21 E Dobrucali, E Sadikoglu, S Demirkesen, C Zhang, A Tezel, (2024). Exploring the impact of COVID-19 on the United States construction industry: Challenges and opportunities. IEEE Transactions on Engineering Management, 71: 1245–1257
https://doi.org/10.1109/TEM.2022.3155055
22 A Ebekozien, C Aigbavboa, (2021). COVID-19 recovery for the Nigerian construction sites: The role of the fourth industrial revolution technologies. Sustainable Cities and Society, 69: 102803
https://doi.org/10.1016/j.scs.2021.102803
23 W H Gan, D Koh, (2021). COVID-19 and return-to-work for the construction sector: Lessons from Singapore. Safety and Health at Work, 12( 2): 277–281
https://doi.org/10.1016/j.shaw.2021.04.001
24 N Gerami Seresht, (2022). Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach. Automation in Construction, 140: 104315
https://doi.org/10.1016/j.autcon.2022.104315
25 P GraduT ZrnicY WangM I Jordan (2022). Valid inference after causal discovery. arXiv preprint arXiv: 2208.05949
26 J Hinze (2004). Construction Planning and Scheduling. NJ: Pearson/Prentice Hall Upper Saddle River
27 A Karamoozian, D Wu, (2024). A hybrid approach for the supply chain risk assessment of the construction industry during the COVID-19 pandemic. IEEE Transactions on Engineering Management, 71: 4035–4050
https://doi.org/10.1109/TEM.2022.3210083
28 W O Kermack, A G McKendrick, G T Walker, (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Containing papers of a mathematical and physical character, 115( 772): 700–721
https://doi.org/10.1098/rspa.1927.0118
29 H F KöhnL J Hubert (2014). Hierarchical cluster analysis, Wiley StatsRef: statistics reference online, 1–13
30 T I Lakoba, D J Kaup, N M Finkelstein, (2005). Modifications of the Helbing-Molnár-Farkas-Vicsek social force model for pedestrian evolution. Simulation, 81( 5): 339–352
https://doi.org/10.1177/0037549705052772
31 J Li, J Zhong, Y M Ji, F Yang, (2021). A new SEIAR model on small-world networks to assess the intervention measures in the COVID-19 pandemics. Results in Physics, 25: 104283
https://doi.org/10.1016/j.rinp.2021.104283
32 M Li, Y Zhao, L He, W Chen, X Xu, (2015). The parameter calibration and optimization of social force model for the real-life 2013 Ya’an earthquake evacuation in China. Safety Science, 79: 243–253
https://doi.org/10.1016/j.ssci.2015.06.018
33 X Liu, (2021). A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan. Results in Physics, 20: 103712
https://doi.org/10.1016/j.rinp.2020.103712
34 H Luo, J Liu, C Li, K Chen, M Zhang, (2020). Ultra-rapid delivery of specialty field hospitals to combat COVID-19: Lessons learned from the Leishenshan Hospital project in Wuhan. Automation in Construction, 119: 103345
https://doi.org/10.1016/j.autcon.2020.103345
35 I Mahmood, H Arabnejad, D Suleimenova, I Sassoon, A Marshan, A Serrano-Rico, P Louvieris, A Anagnostou, S J E Taylor, D Bell, D Groen, (2022). FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions. Journal of Simulation, 16( 4): 355–373
https://doi.org/10.1080/17477778.2020.1800422
36 government Michigan (2022). Outbreak reporting.
37 G Milne, T Hames, C Scotton, N Gent, A Johnsen, R M Anderson, T Ward, (2021). Does infection with or vaccination against SARS-CoV-2 lead to lasting immunity. Lancet. Respiratory Medicine, 9( 12): 1450–1466
https://doi.org/10.1016/S2213-2600(21)00407-0
38 U K Mukherjee, S Bose, A Ivanov, S Souyris, S Seshadri, P Sridhar, R Watkins, Y Xu, (2021). Evaluation of reopening strategies for educational institutions during COVID-19 through agent based simulation. Scientific Reports, 11( 1): 6264
https://doi.org/10.1038/s41598-021-84192-y
39 D Müllner (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.237
40 M Naili, M Bourahla, M Naili, (2019). Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method. International Journal of Simulation and Process Modelling, 14( 1): 97702–97718
https://doi.org/10.1504/IJSPM.2019.097702
41 C Nnaji, Z Jin, A Karakhan, (2022). Safety and health management response to COVID-19 in the construction industry: A perspective of fieldworkers. Process Safety and Environmental Protection, 159: 477–488
https://doi.org/10.1016/j.psep.2022.01.002
42 K Onishi, A Iida, M Yamakawa, M Tsubokura, (2022). Numerical analysis of the efficiency of face masks for preventing droplet airborne infections. Physics of Fluids, 34( 3): 033309
https://doi.org/10.1063/5.0083250
43 A J Onumanyi, D N Molokomme, S J Isaac, A M Abu-Mahfouz, (2022). AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset. Applied Sciences, 12( 15): 7515
https://doi.org/10.3390/app12157515
44 C J ReynoldsL SwadlingJ M GibbonsC PadeM P Jensen M O DinizN M SchmidtD K ButlerO E AminS N L Bailey S M MurrayF P PieperS TaylorJ JonesM Jones W Y J LeeJ RosenheimA ChandranG JoyGenova C Di N TempertonJ LambourneT Cutino-MoguelM AndiapenM Fontana A SmitA SemperB O’BrienB ChainT Brooks C ManistyT TreibelJ C MoonM NoursadeghiD M Altmann M K MainiÁ McKnightR J Boyton (2020). Discordant neutralizing antibody and T cell responses in asymptomatic and mild SARS-CoV-2 infection. medRxiv 2020.10.13.20211763
45 A RossV L Willson (2017). One-way anova. Brill: Basic and advanced statistical tests. Brill: 21–24
46 N SalimW H ChanS MansorN E Nazira BazinS Amaran A A Mohd FaudziA ZainalS H Huspi E K Jiun HooiS M Shithil (2020). COVID-19 epidemic in Malaysia: Impact of lockdown on infection dynamics. medRxiv, 20057463
47 M S Shamil, F Farheen, N Ibtehaz, I M Khan, M S Rahman, (2021). An agent-based modeling of COVID-19: Validation, analysis, and recommendations. Cognitive Computation, 14( 1): 1–12
https://doi.org/10.1007/s12559-020-09801-w
48 F Sierra, (2022). COVID-19: main challenges during construction stage. Engineering, Construction, and Architectural Management, 29( 4): 1817–1834
https://doi.org/10.1108/ECAM-09-2020-0719
49 I M Sticco, G A Frank, C O Dorso, (2021). Social Force Model parameter testing and optimization using a high stress real-life situation. Physica A, 561: 125299
https://doi.org/10.1016/j.physa.2020.125299
50 D Stieler, T Schwinn, S Leder, M Maierhofer, F Kannenberg, A Menges, (2022). Agent-based modeling and simulation in architecture. Automation in Construction, 141: 104426
https://doi.org/10.1016/j.autcon.2022.104426
51 M StoddardD Van EgerenK JohnsonS RaoJ Furgeson D E WhiteR P NolanN HochbergA Chakravarty (2020). Model-based evaluation of the impact of noncompliance with public health measures on COVID-19 disease control. medRxiv, 20240440
52 S SunY Zheng (2021). The research of SEIJR model with time-delay based on 2019-nCov. IEEE Access: Practical Innovations, Open Solutions, 9: 117949–117956
53 C Szabo, Y M Teo, G K Chengleput, (2014). Understanding complex systems: Using interaction as a measure of emergence. Proceedings of the Winter Simulation Conference, 207–218
https://doi.org/10.1109/WSC.2014.7019889
54 County People’s Government Taojiang (2021). Wear masks, travel less, muster less, isolate rigorously and vaccinate quickly.
55 Tribune Tennessee (2020). Metro public health department releases list of area COVID-19 clusters.
56 M Wang, S Flessa, (2020). Modelling COVID-19 under uncertainty: What can we expect?. European Journal of Health Economics, 21( 5): 665–668
https://doi.org/10.1007/s10198-020-01202-y
57 Y Wang, Z Lv, Z Sheng, H Sun, A Zhao, (2022). A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic. Advanced Engineering Informatics, 53: 101678
https://doi.org/10.1016/j.aei.2022.101678
58 State Department of Health Washington (2022). Statewide COVID-19 Outbreak Report.
59 J T Wu, K Leung, M Bushman, N Kishore, R Niehus, P M de Salazar, B J Cowling, M Lipsitch, G M Leung, (2020). Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan. Nature Medicine, 26( 4): 506–510
https://doi.org/10.1038/s41591-020-0822-7
60 Z Xu, H Zhang, Z Huang, (2022). A continuous Markov-Chain model for the simulation of COVID-19 epidemic dynamics. Biology, 11( 2): 190
https://doi.org/10.3390/biology11020190
[1] Zhi-kun Ding,Yi-fei Wang,Jin-chuang Wu. CAS and ABM-based Demolition Waste Management Research in the AEC Industry[J]. Front. Eng, 2016, 3(1): 18-23.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed